AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
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AI for Medicine 专项课程deeplearning.ai课程信息
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第 2 门课程（共 3 门）
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中级
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
完成时间大约为30 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Deep LearningMachine Learningtime-to-event modelingRandom Forestmodel tuning
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
完成时间大约为30 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
教学大纲 - 您将从这门课程中学到什么
完成时间为 9 小时
Linear prognostic models
Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
完成时间为 9 小时
11 个视频 （总计 28 分钟）, 4 个阅读材料, 2 个测验
11 个视频
Prerequisites and Learning Outcomes1分钟
Medical Prognosis2分钟
Examples of Prognostic Tasks2分钟
Atrial fibrillation2分钟
Liver Disease Mortality2分钟
Risk of heart disease2分钟
Risk Score Computation4分钟
Evaluating Prognostic Models1分钟
Concordant Pairs, Risk Ties, Permissible Pairs2分钟
C-Index3分钟
4 个阅读材料
Connect with your mentors and fellow learners on Slack!10分钟
Please save your work regularly10分钟
About the automatic grader10分钟
How to refresh your workspace10分钟
1 个练习
Week 1 Quiz30分钟
完成时间为 7 小时
Prognosis with Tree-based models
Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
完成时间为 7 小时
15 个视频 （总计 41 分钟）
15 个视频
Decision trees1分钟
Dividing the input space2分钟
Building a decision tree2分钟
How to fix overfitting4分钟
Survival Data3分钟
Different distributions2分钟
Missing Data example2分钟
Missing completely at random2分钟
Missing at random3分钟
Missing not at random3分钟
Imputation1分钟
Mean Imputation4分钟
Regression Imputation2分钟
Calculate Imputed Values2分钟
1 个练习
Week 2 Quiz30分钟
完成时间为 6 小时
Survival Models and Time
This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
完成时间为 6 小时
16 个视频 （总计 38 分钟）
16 个视频
Survival Function2分钟
Valid survival functions3分钟
Collecting Time Data1分钟
When a stroke is not observed2分钟
Heart Attack Data2分钟
Right censoring1分钟
Estimating the survival function1分钟
Died immediately, or never die3分钟
Somewhere in-between1分钟
Using censored data1分钟
Chain rule of conditional probability2分钟
Deriving Survival2分钟
Calculating Probabilities from the Data3分钟
Comparing Estimates3分钟
Kaplan Meier Estimate2分钟
1 个练习
Week 3 Quiz30分钟
完成时间为 8 小时
Build a risk model using linear and tree-based models
This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
完成时间为 8 小时
24 个视频 （总计 69 分钟）, 3 个阅读材料, 2 个测验
24 个视频
Hazard3分钟
Survival to hazard2分钟
Cumulative Hazard3分钟
Individualized Predictions3分钟
Relative risk3分钟
Ranking patients by risk1分钟
Individual vs. baseline hazard2分钟
Smoker vs. non-smoker2分钟
Effect of age on hazard3分钟
Risk factor increase per unit increase in a variable1分钟
Risk Factor Increase or Decrease4分钟
Intro to Survival Trees4分钟
Survival tree5分钟
Nelson Aalen estimator5分钟
Comparing risks of patients1分钟
Mortality score2分钟
Evaluation of Survival Model3分钟
Permissible and Non-Permissible Pairs2分钟
Possible Permissible Pairs1分钟
Example of Harrell's C-Index3分钟
Example of Concordant Pairs2分钟
Week 4 Summary47
Congratulations!1分钟
3 个阅读材料
Congratulations on finishing course 2!10分钟
Acknowledgements10分钟
Citations10分钟
1 个练习
Week 4 Quiz30分钟
审阅
来自AI FOR MEDICAL PROGNOSIS 的热门评论
由 JD 提供Jun 4, 2020
I am a medical image analysis enthusiast. But I always wonder why I can't I combine other patient details for extending it's application. Sure this course is awesome. I really loved it !!
由 BM 提供Apr 21, 2020
This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.
由 RR 提供Sep 8, 2020
This course is one of the best courses to learn about Medical Prognosis. Really, the survival models were described in great detail. Thank you, Pranav for this wonderful course.
由 JH 提供May 1, 2020
Excellent course! Real world data and robust models. Of particular value was the implementation of the SHAP feature interpretation algorithm as applied to ensemble models.
关于 AI for Medicine 专项课程
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
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